// Update the Eager Executor for current thread.
  virtual void SetExecutorForThread(EagerExecutor* executor) = 0;

  // Return a list of local tensorflow::Device*.
  // TODO(tfrt-devs): We shouldn't expose legacy device in this API.
  virtual std::vector<tensorflow::Device*> ListLocalTfDevices() = 0;

  // Return a list of all tensorflow::Device*.
  virtual std::vector<tensorflow::Device*> ListAllTfDevices() = 0;

  char buffer[BUFFER_SIZE];
  std::strncpy(buffer, "VALID", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "padding", buffer, std::strlen(buffer));
  // Overwriting value in "buffer", should be fine since TFE_Op
  // shouldn't be holding on to it.
  std::strncpy(buffer, "NHWC", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "data_format", buffer, std::strlen(buffer));

  TFE_OpSetAttrType(op, "T", TF_FLOAT);

    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TF_DeleteStatus(status);
  }

  // 6. Read `var` using `ctx_1`. This read should succeed since `ctx_1` was
  // created with `isolate_session_state` set to false, and update should
  // preserve it.
  {
    // Create a handle to `var`, using `ctx_1`.
    TFE_TensorHandle* var_handle =

}

void NoOpDeallocator(void* data, size_t, void*) {}

TEST(CAPI, MalformedTensor) {
  // See https://github.com/tensorflow/tensorflow/issues/7394
  // num_dims = 0 implies a scalar, so should be backed by at least 4 bytes of
  // data.
  TF_Tensor* t =
      TF_NewTensor(TF_FLOAT, nullptr, 0, nullptr, 0, &NoOpDeallocator, nullptr);
  ASSERT_TRUE(t == nullptr);
}

TEST(CAPI, AllocateTensor) {

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_AbstractOpSetOpName(placeholder_op, "my_ph", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // This should fail.
  TF_AbstractOpSetOpType(placeholder_op, "Placeholder", status.get());
  ASSERT_EQ(TF_FAILED_PRECONDITION, TF_GetCode(status.get()));

  TF_DeleteAbstractOp(placeholder_op);
  TF_DeleteExecutionContext(graph_ctx);

  void Watch(int64_t tensor_id, Gradient* tangent);

  // Removes the gradient associated with tensor_id. Should be called when the
  // Tensor associated with `tensor_id` is deleted.
  void DeleteGradient(int64_t tensor_id);

  // Runs forward autodiff. Should be called whenever a new operation is
  // available and the accumulator is active.
  //

    importlib.import_module("tf_keras.src.optimizers")
  else:
    importlib.import_module("keras.src.optimizers")
except (ImportError, AttributeError):
  pass

del importlib

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied

#      Ex. 1: TFCI=py311,linux_x86_cuda,nightly_upload  (nightly job)
#      Ex. 2: TFCI=py39,linux_x86,rbe                   (continuous job)
#   Non-Googlers should replace "nightly_upload" or "rbe" with
#   "public_cache,disk_cache".
#   Googlers should replace "nightly_upload" with "public_cache,disk_cache" or
#   "rbe", if you have set up your system to use RBE (see further below).
#
# Here is how to choose your TFCI value:

    string prefix_cmp;
    const char* child_scope_name;
    if (prefix == nullptr) {
      child_scope_name = "gradients";
    } else {
      prefix_cmp = string(prefix) + "/";
      // The operation should fail if the provided name prefix has already been
      // used in this graph
      for (const auto& pair : g->name_map) {
        const string& name = pair.first;

    TFE_MonitoringCounterCell* cell);

// APIs for Counter without label.
typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0;
// Returns a new Counter metric object. The caller should manage lifetime of
// the object. Using duplicate metric name will crash the program with fatal
// error.
TF_CAPI_EXPORT extern TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(